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Record W2130595127 · doi:10.1021/ar030252f

Ionic-Liquid-Supported Synthesis:  A Novel Liquid-Phase Strategy for Organic Synthesis

2006· article· en· W2130595127 on OpenAlexaff
Weishi Miao, Tak Hang Chan

Bibliographic record

VenueAccounts of Chemical Research · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsIonic liquidReagentChemistrySolubilityOrganic synthesisPhase (matter)Aqueous solutionCatalysisCombinatorial chemistryIonic bondingAqueous two-phase systemOrganic chemistryChemical engineeringIon

Abstract

fetched live from OpenAlex

Soluble ionic liquids have recently been used as supports for catalyst/reagent immobilization and synthesis in homogeneous solution phase. The wide range of ionic liquid supports available makes their use as supports compatible with most common chemistries. The solubility properties of these ionic liquid supports can be tuned by the variation of cations and anions to make them phase separate from less polar organic solvents and aqueous media. The ionic-liquid-supported species can therefore be purified from the reaction mixture by simple washings. Ionic-liquid-supported catalysts and reagents have been prepared and used, and they are easily recovered and reused. Parallel and combinatorial libraries of small molecules have been synthesized. Ionic-liquid-supported synthesis (ILSS) has been applied to the preparation of oligopeptides and oligosaccharides. The comparison of ILSS with solid-phase synthesis, soluble-polymer-supported synthesis, and fluorous phase synthesis has been highlighted where applicable.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.371
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations433
Published2006
Admission routes1
Has abstractyes

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